Data-Driven Proactive Inventory Policies Under Demand and Supply Uncertainty in Global Supply Chains

Managing global supply chains presents significant challenges due to the inherent complexities in large networks of suppliers, manufacturers, transportation providers, distributors, retailers, and end consumers. To make the situation worse, supply chain managers face additional challenges due to demand and/or supply-related disruptions, and the ensuing delays and their ripple effects. To address these issues, previous research has proposed both reactive and proactive strategies, such as redundant sourcing, logistics flexibility, and shipment rerouting. Among these, inventory policies play a central role by allowing adjustments to lot sizes and safety stock levels in response to delays. By leveraging real-time visibility, these policies enable the efficient detection of delays and the optimization of inventory allocation. This chapter explores the value of such data-driven inventory policies in improving the performance of global supply chains, specifically when unexpected adverse events like port closures or transportation disruptions occur. In doing so, we develop a simulation model assuming that there is sufficient supply chain visibility that allows the firm to monitor inventory levels and to get real-time updates regarding shipment locations and order progress. The findings demonstrate that data-driven policies lead to some improvements in inventory performance, resulting in higher service levels.